Topic: lateral connections

Spatial context plays an important role in visual processing and perception. A rich set of surround effects that involve nonlinear interactions between the center and surround have been found in Primary Visual Cortex (V1). A general summary is that surround suppression is prevalent and the amount of suppression depends on the visual similarity between the center and surround. In recent years, convolutional neural networks (CNNs) have successfully modeled neural behavior across the visual cortices. However, there have been limited studies addressing the multitude of contextual effects that have

The visual system is characterized by multiple mirrored visuotopic maps, with each repetition corresponding to a different visual area. In this work we explore whether such visuotopic organization can emerge as a result of minimizing the total wire length between neurons connected in a deep hierarchical network. Our results show that networks with purely feedforward connectivity typically result in a single visuotopic map, and in certain cases no visuotopic map emerges. However, when we modify the network by introducing lateral connections, with sufficient lateral connectivity among neurons wi

Dynamic Causal Modeling (DCM) is commonly used for identifying neural circuit models from neuroimaging data to deduce causal neural interactions (Penny et al., 2011). Although previous studies have applied DCM to decision-making tasks, it is unclear whether DCM can reveal multistable dynamic states associated with choices (Wong and Wang, 2006). Here, we developed a two-column cortical fully self-feedback neural mass model (FSM) comprising two excitatory and one inhibitory neural populations, and inputs to both inhibitory and excitatory populations (Roach et al., 2023) with lateral connections